{"id":"W4407285098","doi":"10.1093/jcag/gwae059.090","title":"A90 ASSESSMENT OF A MULTI-COMPONENT QUALITY IMPROVEMENT INTERVENTION TO IMPROVE DIAGNOSTIC YIELD FROM ENDOSCOPIC ULTRASOUND-GUIDED FINE NEEDLE ASPIRATION BIOPSY OF SOLID MASS LESIONS","year":2025,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Alberta","funders":"","keywords":"Endoscopic ultrasound; Fine-needle aspiration; Radiology; Medicine; Yield (engineering); Biopsy; Component (thermodynamics); Intervention (counseling); Materials science; Composite material; Nursing; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01866885,0.0006515988,0.0006983724,0.001145538,0.0008443169,0.001394466,0.0009871183,0.0006369432,0.001908026],"category_scores_gemma":[0.02926588,0.0002968241,0.001113791,0.001064734,0.0004862955,0.0007651417,0.001367982,0.0006430271,0.0001573588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003309302,"about_ca_system_score_gemma":0.009233057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003584864,"about_ca_topic_score_gemma":0.004160616,"domain_scores_codex":[0.9816965,0.01174559,0.001719558,0.0009835111,0.002819705,0.001035106],"domain_scores_gemma":[0.9729818,0.009416833,0.00835512,0.00139218,0.003997481,0.003856733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0195156,0.04114451,0.1793189,0.004128737,0.0009422345,0.0002071775,0.002343045,0.003428392,0.004941266,0.0004153077,0.00260504,0.7410097],"study_design_scores_gemma":[0.01476417,0.1456828,0.8145005,0.001302387,0.001627414,0.0002166409,0.001701325,0.009546242,0.006198682,0.0002927159,0.004048382,0.0001187974],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840073,0.001023852,0.003684748,0.00112524,0.0000888811,0.007088291,0.0002289972,0.000270657,0.002482004],"genre_scores_gemma":[0.9853384,0.0003372736,0.01151515,0.000265775,0.00004199021,0.002119753,0.0001297534,0.000007552102,0.0002443149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01866885,"threshold_uncertainty_score":0.09873146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058377754108755,"score_gpt":0.3276685427507285,"score_spread":0.307084765209641,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}